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Predictive policing, the practice of using of algorithmic systems to forecast crime, is heralded by police departments as the new frontier of crime analysis. At the same time, it is opposed by civil rights groups, academics, and media outlets for being ‘biased’ and therefore discriminatory against communities of color. This paper argues that the prevailing focus on racial bias has overshadowed two normative factors that are essential to a full assessment of the moral permissibility of predictive policing: fairness in the social distribution of the benefits and burdens of policing as well as the distinctive role of consent in determining fair distribution. When these normative factors are given their due attention, several requirements emerge for the fair implementation of predictive policing. Among these requirements are that police departments inform and solicit buy-in from affected communities about strategic decision-making and that departments favor non-enforcement-oriented interventions.
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A Machine Learning Evaluation Framework for Place-based Algorithmic Patrol Management
While the social and ethical risks of PAPM have been widely discussed, little guidance has been provided to police departments, community advocates, or to developers of place-based algorithmic patrol management systems (PAPM systems) about how to mitigate those risks. The framework outlined in this report aims to fill that gap. This document proposes best practices for the development and deployment of PAPM systems that are ethically informed and empirically grounded. Given that the use of place-based policing is here to stay, it is imperative to provide useful guidance to police departments, community advocates, and developers so that they can address the social risks associated with PAPM. We strive to develop recommendations that are concrete, practical, and forward-looking. Our goal is to parry critiques of PAPM into practical recommendations to guide the ethically sensitive design and use of data-driven policing technologies.
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- Award ID(s):
- 1917712
- PAR ID:
- 10480922
- Publisher / Repository:
- Social Science Research Network
- Date Published:
- Journal Name:
- SSRN Electronic Journal
- ISSN:
- 1556-5068
- Subject(s) / Keyword(s):
- Machine Learning Ethics Law Enforcement Policing
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2139/ssrn.4549861
